Physiological measurement logic engine

ABSTRACT

A patient monitor including a physiological measurement logic engine receives physiological data from a physiological sensor. The logic engine abstracts one or more features of the physiological data and determines a category for the abstracted feature. The logic engine further encodes the category of each of the one or more features and determines an action to perform based on the encoded categories.

RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 13/272,038, filed Oct. 12, 2011, entitled “PhysiologicalMeasurement Logic Engine,” which claims priority benefit of U.S.Provisional Patent Application No. 61/392,863, filed Oct. 13, 2010,entitled “Physiological Measurement Logic Engine,” each of which ishereby incorporated by reference herein in its entirety.

FIELD

The present disclosure relates to non-invasive biological parametersensing, including sensing using optical and/or acoustic sensors andrelated systems and methods.

BACKGROUND

Patient monitoring of various physiological parameters of a patient isimportant to a wide range of medical applications. Pulse oximetry is oneof the techniques that have developed to accomplish the monitoring ofsome of these physiological characteristics. Pulse oximetry relies on asensor attached externally to a patient to output signals indicative ofvarious physiological parameters, such as a patient's constituentsand/or analytes, including for example a percent value for arterialoxygen saturation, carbon monoxide saturation, methemoglobin saturation,fractional saturations, total hematocrit, billirubins, perfusionquality, or the like. A pulse oximetry system generally includes apatient monitor, a communications medium such as a cable, and/or aphysiological sensor having light emitters and a detector, such as oneor more LEDs and a photodetector. The sensor is attached to a tissuesite, such as a finger, toe, ear lobe, nose, hand, foot, or other sitehaving pulsatile blood flow which can be penetrated by light from theemitters. The detector is responsive to the emitted light afterattenuation by pulsatile blood flowing in the tissue site. The detectoroutputs a detector signal to the monitor over the communication medium,which processes the signal to provide a numerical readout ofphysiological parameters such as oxygen saturation (SpO2) and/or pulserate.

High fidelity patient monitors capable of reading through motion inducednoise are disclosed in U.S. Pat. Nos. 7,096,054, 6,813,511, 6,792,300,6,770,028, 6,658,276, 6,157,850, 6,002,952 5,769,785, and 5,758,644,which are assigned to Masimo Corporation of Irvine, Calif. (“MasimoCorp.”) and are incorporated by reference herein. Advanced physiologicalmonitoring systems can incorporate pulse oximetry in addition toadvanced features for the calculation and display of other bloodparameters, such as carboxyhemoglobin (HbCO), methemoglobin (HbMet),total hemoglobin (Hbt), total Hematocrit (Hct), oxygen concentrations,glucose concentrations, blood pressure, electrocardiogram data,temperature, and/or respiratory rate as a few examples. Typically, thephysiological monitoring system provides a numerical readout of and/orwaveform of the measured parameter. Advanced physiological monitors andmultiple wavelength optical sensors capable of measuring parameters inaddition to SpO2, such as HbCO, HbMet and/or Hbt are described in atleast U.S. patent Ser. No. 11/367,013, filed Mar. 1, 2006, titledMultiple Wavelength Sensor Emitters, now issued as U.S. Pat. No.7,764,982, and U.S. patent application Ser. No. 11/366,208, filed Mar.1, 2006, titled Noninvasive Multi-Parameter Patient Monitor, assigned toMasimo Laboratories, Inc. and incorporated by reference herein. Further,noninvasive blood parameter monitors and optical sensors includingRainbow™ adhesive and reusable sensors and RAD-57™ and Radical-7™monitors capable of measuring SpO2, pulse rate, perfusion index (PI),signal quality (SiQ), pulse variability index (PVI), HbCO and/or HbMet,among other parameters, are also commercially available from MasimoCorp. of Irvine, Calif.

Another physiological monitoring system uses sensors that includepiezoelectric membranes located on or near a patient's body to measurebody sounds. The body sounds can then be analyzed to determineventilation, apnea, respiration rate, or other parameters. Thesemonitors are referred to as acoustic respiratory monitors. Acousticrespiratory monitors are also commercially available from Masimo Corp.of Irvine, Calif.

SUMMARY

The present disclosure relates to a system for simplifying logic choicesin a computing environment. In an embodiment physiological processing issimplified by abstracting relevant features, or general characteristicsof the signal. As used herein, features and general characteristics areused interchangeably. In an embodiment, features of physiologicalsignals are abstracted and are used in conjunction with a logic table inorder to determine a course of action. In an embodiment, the abstractedfeatures are used to provide a bit encoding scheme which directlyrelates to a specified result. In an embodiment, the system is used toencode logic choices relevant to display characteristics of the device.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of an abstraction of the relative slopesof physiological signals.

FIG. 2 illustrates an example of an abstraction of the difference inoutputs of different processing engines.

FIG. 3 illustrates an example of an abstraction of a trend line linearregression, and/or a standard deviation.

FIG. 4 is an expanded view of a lookup table that can be used by apatient monitor to determine what action to perform in view of theabstracted features.

FIG. 5 is a flow chart illustrating a process implemented by a patientmonitor for determining what action to perform based on the abstractedfeatures.

FIG. 6 is a flow chart illustrating an embodiment of a processimplemented by a patient monitor for determining an appropriate actionbased on the features of a set of data.

FIG. 7 illustrates an embodiment of a patient monitoring system capableof abstracting features of physiological data.

FIG. 8 illustrates an example noninvasive multiparameter physiologicalmonitor.

FIG. 9 illustrates an embodiment of an acoustic sensor system, which canprovide physiological data to the patient monitoring system.

DETAILED DESCRIPTION

Signal Analysis

Real time processing of physiological signals is often difficult andrequires significant computing power, fast processors and significantpower consumption and heat dissipation. Typical signal processinginvolves intensive and difficult mathematic operations in order toextrapolate useful data.

Feature Abstraction

One way to reduce the computational load in a physiological processingsystem is to abstract features of the signal. Feature abstractions useonly relatively simple analysis which significantly reducescomputational loads. Once abstracted, the features can then be used tomake determinations about the physiological signals.

The difference between feature abstraction and typical computationallyintensive signal processing is best understood with an analogy to humanobservations vs. computations. For example, consider the situation wherePerson A lives next to Person B. Person B brings their trash containerout to the same position every time the trash is collected. AlthoughPerson A may not consciously note the position of the trash container,Person A is likely to notice that something is different if the trashcontainer is placed in a different position. Importantly, thatrecognition can be made without measuring the exact position change ofthe trash container or even consciously noting the normal position ofthe trash container. Similarly, Person A may further note other changesregarding Person B. For example, Person A is likely to note that adifferent car is parked in Person B's driveway. Person A can make thisdetermination without comparing old and new license plate numbers.Another example may be that Person A notes different children playing inPerson B's yard. None of these abstractions or general observationsrequired significant thought or specific measurements on the part ofPerson A. Similarly, features of physiological signals can be abstractedat various levels without significant computations.

Once abstracted, each feature can potentially indicate a number ofpossible explanations. For example, using the same analogy above, thechange in location of Person B's trash container could indicate that adifferent family member took out the trash. However, coupling the changein location of trash container along with the different car andunfamiliar children playing in the yard may indicate that Person A hasnew neighbors. Importantly, the conclusion that Person A may have newneighbors is made without having direct knowledge of a change inneighbors or actually seeing the neighbors. Similarly, combinations ofabstracted features provide different indications about the signal underanalysis, while using relatively low processing power. However, it is tobe understood that abstracting features of the signals described hereinis significantly more complex and computationally intensive than theexample given above. Furthermore, the abstraction of features of thesignals (or data) described herein is typically done in real-time ornear real-time using a digital signal processor, microcontroller, orother processor, operating at speeds far surpassing those of a human.For example, the processor may operate at hundreds, thousands, millions,billions, or even more cycles per second to ensure the features areabstracted in a timely manner. If the features are abstracted too slowlythey lose their relevance.

In an embodiment, various features of a detected signal are abstracted.For example, in an embodiment, the relative slope of a signal over oneor more windows of time is abstracted. In an embodiment, the relativenoise level of a signal is determined. In an embodiment, the relativesignal strength is determined. In an embodiment, comparisons betweenvarious features over different windows of time are compared and thecomparison is an abstracted feature. In an embodiment, the features areabstracted in real-time or near real-time. Other abstractions can alsobe made as will be understood by those of skill in the art based on thepresent disclosure.

The examples provided below in FIGS. 1-3 are simplified examples of thetypes of abstractions that occur using the device described herein. Itis to be understood that the calculations, computations, andabstractions are performed in real-time or near real-time at speedssurpassing those capable of a human. For example, the processor mayperform hundreds, thousands, millions, billions or more calculations persecond to determine the appropriate abstraction in real time or nearreal-time. In addition, the signals (or data) abstracted by theprocessor may be electrical signals, infrared signals, wireless signals,or other electro-magnetic wave signals that are incomprehensible to ahuman in their raw form and at the speeds communicated.

FIG. 1 illustrates an example of an abstraction of the relative slopesof physiological signals. As illustrated in FIG. 1, signals within twowindows of time 102, 104 are analyzed. The windows can be any length oftime. In an embodiment, each window is 30 seconds long. In anotherembodiment, each window is 60 seconds long. Other lengths of time canalso be used. The windows can overlap or be non-overlapping in time. Thesignals in each window are then abstracted and characterized into one offive different slopes. These slopes are illustrated by the slopeabstraction illustrator 114. For example, the relative slope of thesignal within window 102 most closely matches slope 118. Similarly, theoverall slope of the signal, or data, within window 104 most closelymatches slope 130. By abstracting the overall slope of the signal into arelative slope, computations relevant to the signals are simplified. Theoverall slope of the signal may also be referred to as the trend of thesignal. Although described with respect to five different slopeabstraction values, more or fewer abstractions levels can be used. Forexample, one system might use 10 different abstraction slopes whileanother system might use three different abstraction slopes.

In an embodiment, after abstraction, the signal in each window isassigned a category. In the embodiment illustrated, the category is abit code corresponding to the slope abstraction. In an embodiment, theslope of the signal in window 102, is matched to slope 118, and assignedbits “001.” Similarly, the slope of the signal in window 104 is assignedto bits “010.” As will be explained in greater detail below, bitassignments can be used to further simplify processing. Furthermore, aswill be understood by those in the art from the present disclosure, thebit codes presented in the this example are not intended to be limitingand other bit codes, including different numbers of bits, can be usedwith the present disclosure.

In an embodiment, after abstracting the slopes of two or more signals intwo or more windows of time, the slopes are then compared in order todetermine a change in slope. The change in relative slope then providesanother abstraction that can be used in further processing to makedeterminations regarding the signal. In an embodiment, this abstractionis also assigned a bit code based on the comparison. In an embodiment,the comparison is not necessary because the bit codes can be used inconjunction with the table described in FIG. 4 to designate an outcomebased on the slopes without the necessity of further comparisoncomputations. Thus, as described below, using the bit codes inassociation with FIG. 4 obviates the need the further processing andcomputational steps that might otherwise be necessary.

Another abstraction involves the comparison of two overlapping windowsof data. This is illustrated, for example, in FIG. 2. Typically,physiological signals received from sensors are processed before beingdisplayed. In processing the physiological signals, a patient monitor,described in more detail below with reference to FIGS. 7 and 9, performsany number of functions, computations, filters, and the like. Theprocessing can account for signal noise, motion artifact, or any numberof other signal distortions that can affect signal quality and thereliability of the output.

In an embodiment a patient monitor processes the data using one or moreprocessing techniques, which may also be referred to as engines orprocessing engines, in parallel. The various engines can identify andfilter specific signal distortions. For example, one engine can beconfigured to filter according to repetitive motion, while anotherengine can be configured to filter according to a single instance ofmotion. Other engines may be configured to filter or process the signalsto account for other signal distortions, such as low signal-to-noiseratio, low signal power, low perfusion and the like. The use of parallelengines is described in U.S. Pat. No. 6,157,850, the disclosure of whichis hereby incorporated by reference in its entirety.

With continued reference to FIG. 2, window 202 is an illustration of aphysiological signal processed over a first time period, or window oftime. Window 204 is an illustration of the same physiological signalprocessed over a different window of time, but that overlaps with thefirst window of time. This can be done using the same or differentengines for each window of time. The x-axis 212 of the windows 202, 204represents time, and the y-axis 214 of the windows 202, 204 representssignal amplitude. The data 220, 222 can be any type of physiologicaldata, signal, or other data. Furthermore, the windows 202, 204 can bebroken down into time the segments 210A, 210B, 210C, 210D, that dividethe data based on a predefined time period. The predefined time periodmay be any size, ranging from less than a microsecond to minutes, hoursor more. Typically, the data within the same time segments 210A, 210B,210C, 210D of different windows 202 and 204 is similar. As noted in FIG.2, the data 220, 222 is generally similar throughout most of the timesegments 210A, 210B, 210C, 210D. However, in time segment 210A, data 206and 208 are substantially dissimilar. The dissimilarities may be aresult of differences in the processing. The dissimilarities may also bethe result of different engines, where one engine is more suited forhandling an event that occurred during time segment 210A, poor signalstrength during time segment 210A, an error, or the like. Thus, onefeature that the patient monitor can abstract is the difference inoutputs between different engines and/or different windows of time.

In an embodiment, while abstracting the data, the patient monitorcompares and identifies the difference between data 206 and data 208 intime segment 210A. The patient monitor categorizes the differencedepending on various factors, such as type of data being analyzed, typeof analysis being performed, engines being used, etc. In an embodiment,the difference is categorized as an insignificant difference, a minordifference, and/or significant difference. Based on the categorization,the patient monitor can implement a predefined action using a look-uptable, which will be described in greater detail below, with referenceto FIG. 4. Additional methods for categorizing the differences betweenthe engine outputs may be used without departing from the spirit andscope of the description. Furthermore, the categories may be encodedusing any number of different bit codes, as described above.

FIG. 3 is a plot diagram illustrating another embodiment of a featureabstraction, involving a trend line, linear regression, and/or standarddeviation. Graph 300 illustrates an example of data measurements 306over time. The x-axis 302 of graph 300 represents time and the y-axis304 represents the amplitude of the measurements 306. The line 308represents a trend line of the data measurements 306 over time. In anembodiment, the data measurements are normalized.

In an embodiment, the patient monitor computes a confidence value of thedata measurements 306. The confidence value can be computed using thestandard deviation, average, correlation coefficient and/or linearregression of the data measurements 306. For example, a high standarddeviation and/or low correlation coefficient may be equated with a lowconfidence value, whereas a low standard deviation and/or highcorrelation coefficient may be equated with a high confidence value.Based on the confidence value, the data can be sorted into differentcategories indicating the level of confidence that can be placed in thedata. For example, a relatively low confidence level may indicate thatthe signals are experiencing relatively large amounts of noise or otherdistortions, and that little confidence should be placed in the output.A relatively high confidence level may indicate that the patient monitoris experiencing relatively little noise in the system, and that highconfidence can be placed in the output. The categories may beimplemented using bit codes, described above with reference to FIG. 1.It is to be understood that more or fewer categories can be used tocategorize the standard deviation of the data, or the trend line.

FIG. 4 is a block diagram illustrating an embodiment of an expanded viewof an electronic lookup table (LUT) 400 implemented in the patientmonitor. In an embodiment, the LUT 400 includes at least three sections:the features logic section 402, the expansion section 404, and theoutput section 406. The features logic section 402 is further brokendown into three subsections 408, 410, 412. Each subsection 408, 410, 412includes a bit encoding of the category of one abstracted feature. Eachbit encoding is made up of multiple bits and/or bytes 414. The LUT 400can be used by the patient monitor to determine what action to performbased on the category, or bit code, of the various features of thephysiological data. It will be understood that the LUT 400 is only anexample, and other embodiments with fewer or more sections, subsections,bit encodings, and features may be used without departing from thespirit and scope of the description.

As mentioned, LUT 400 includes three sections: the features logicsection 402, the expansion section 404, and the output section 406. Thepatient monitor uses the feature logic section 402 and the expansionsection 404 to “lookup” the action (encoded as the output 406) that isto be performed. Thus, each possible permutation of the featured logicsection 402 and the expansion section 404 can have a correspondingoutput section 406. In other words, the output section 406 (or action)is selected as a function of the featured logic section 402 and theexpansion section 404.

The feature logic section 402 is made up of one or more subsections 408,410, and 412. Each subsection 408, 410, 412 can include one or morerepresentations of categories of individual features in the form ofindividual bits and/or bytes 414. In the example illustrated, thefeatures logic 402 includes three subsections 408, 410, 412. Eachsubsection 408, 410, 412 includes a bit code, made up of two bits, for acategory of one individual feature. It will be understood that thefeature logic section 402 can include fewer or more subsections and thatthe categories of the individual features may be represented with moreor fewer bits as desired. For example, a greater number of categoriesmay be desired for some features based on their complexity. As such, thefeatures having more categories can use larger bit codes with more bitsor bytes. Accordingly, in an embodiment, the bit codes for the differentfeatures are not uniform in their size. For example, one bit code forone feature may use two bits, while another bit code for another featuremay use five bytes. In another embodiment, the bit codes are uniform insize for all features.

The expansion section 404 can include a number of subsections, similarto the subsections 408, 410, 412 of the feature logic section 402. Theexpansion subsections can include space, in the form of bits/bytes, fornew features that are not included in the subsections 408, 410, 412.When not being used, the bits/bytes in the expansion section 404 can allbe set to a logic ‘0’ or logic ‘1,’ as desired.

As mentioned earlier, the output section 406 is used by the patientmonitor to determine the appropriate action in light of the featurelogic section 402 and the expansion section 404. The patient monitor canuse other logic as well in determining the appropriate output or action.The output section 406 can include a number of subsections similar tothe feature logic section 402 and the expansion section 404.Furthermore, the actions to be taken by the patient monitor are encodedas bit codes within the output section 406. In an embodiment, eachpermutation of the feature logic section 402 and the expansion section404 equates to a different bit code in the output section 406. Inanother embodiment, the bit code in the output section 406 for one ormore permutations of the feature logic section 402 and the expansionsection 404 is the same.

By abstracting the features and using the LUT 400, the patient monitorcan reduce the amount of processing resources needed to perform theappropriate action given the set of data. Rather than processing thedata itself, the patient monitor is able to abstract generalizations orgeneral characteristics of the data and make determinations based on thegeneral characteristics themselves. Thus, the patient monitor avoidsprocessing the individual data itself. Even in those instances whereanalyzing or determining a feature is resource intensive, the patientmonitor is able to reduce the overall amount of processing by reducingthe number of items analyzed. For instance, instead of processinghundreds or even thousands of individual pieces of data, the patientmonitor is able to process all, or a large number of, the pieces of datausing a relatively small number of general characteristics that apply tothe pieces of data in the aggregate. In addition, the use of a lookuptable allows the actions or outputs to be predetermined, allowing thepatient monitor to perform a simple “lookup” rather than repeatedlydetermining the appropriate action for each feature or piece of dataanalyzed. Furthermore, the lookup table can be implemented in hardware,further saving processing resources. Another benefit of the table isthat in one embodiment there are no conditions left undefined. Often, ingenerating large and complex if/then statements in software, conditionsare inevitably left out such that the device does not know what to dounder an undefined condition. The table obviates this problem byinherently providing a result for every possible state.

FIG. 5 is a flow chart illustrating a process 500 implemented by apatient monitor for determining what action to perform based on theabstracted features. In an embodiment, the process 500 is executed inreal-time or near real-time by the patient monitor. At block 504, thepatient monitor obtains physiological data. The physiological data canbe many various types of physiological data as described above. In someembodiments, the data need not be physiological data, as will bedescribed in greater detail below.

At block 506, the patient monitor abstracts features of a set of data,or general characteristics. As described above, the features mayinclude: differing engine outputs, standard deviation, slope, average,linear regression, correlation coefficient, and the like. Additionalfeatures may be used as well, such as time domain features, frequencydomain features, and the like.

In abstracting the features, the patient monitor may analyze variousgeneral characteristics of the set of data in a variety of ways. Forexample, the patient monitor can abstract all the features or a subsetof all the features. The subset can be determined based on the featuresthat require, or are likely to use, relatively little processingresources, or can be determined randomly. In an embodiment, the patientmonitor uses a list of predetermined features to determine whichfeatures to analyze. In an embodiment, the list of features is stored inthe memory of the patient monitor. In another embodiment, the list isstored in memory remotely located from the patient monitor. In yetanother embodiment, the patient monitor determines which features are tobe abstracted based on the type of data being processed or based on theabstractions that are available to the monitor at any given point intime. For example, some features of data may be more pronounced or moreeasily determined based on the type of data received. For example,comparing the output of different engines of plethysmograph data may beless computationally intensive than calculating the standard deviationor linear regression of the plethysmograph data. In such an instance,the patient monitor can select to abstract the comparison between thedata engines and not calculate the standard deviation or linearregression. In another embodiment, the patient monitor determines bothabstractions. In an embodiment, the determination of which abstractionto use is based on a confidence level for each abstraction. In anembodiment, each abstraction is further given a confidence bit code toindicate a confidence level of that abstraction.

At block 508, the patient monitor matches the features of the set ofdata with an appropriate output, or action using a lookup table. Thelookup table may be similar to the one described above, with referenceto FIG. 4. Although not shown, the patient monitor can encode thefeatures into different categories prior to using the lookup table. Uponcompleting the lookup and performing the associated action, the process500 repeats itself as needed throughout the operation of the device.

FIG. 6 is a flow chart illustrating an embodiment of a process 600implemented by a patient monitor for determining an appropriate actionbased on the features of physiological data, which can also be referredto as a set of data.

At block 604, the patient monitor obtains a set of data as described ingreater detail above, with reference to block 502 of FIG. 5. At block606, the patient monitor determines a first feature of the set of data.As described earlier, the feature can be any one of various generalcharacteristics of the data including, slope, standard deviation, linearregression, correlation coefficient, differences between engine outputs,time domain, frequency domain, and the like. It is to be understood thatfeatures of data that can be abstracted are many, and should not belimited by the features specifically recited herein.

At block 608, the patient monitor determines a category within the firstfeature of the set of data. As described earlier, with reference toFIGS. 1, 2, and 3, the categories may take many different forms. Forinstance, as described above with reference to FIG. 1, if the abstractedfeature is the slope of the set of data, the categories may berepresented as a number of different slopes, and encoded using bitcodes. Thus, the patient monitor will determine the proper slope, orcategory, that should be associated with the set of data.

In the example illustrated in FIG. 2, the abstracted feature is thedifference between engine outputs, and the categories can beinsignificant difference, minor difference, and significant difference.Thus, the patient monitor determines which category is appropriate forthe difference between data 206 and 208 in time segment 210A of FIG. 2.

Similarly, the patient monitor can determine the appropriate categoryfor the standard deviation, linear regression, correlation coefficient,and/or trend line of data measurements, as described above withreference to FIG. 3. In an embodiment, the number of differentcategories within a feature is finite and/or predetermined. In anotherembodiment, the categories are determined dynamically during process600. In an embodiment, the categories are implemented using bit codes.In another embodiment, the categories are implemented using analphanumeric code, or word. Furthermore, the categories of each featuremay be different. For instance, one feature may have two categories andanother feature may have ten or more features. Furthermore, thedifferent categories of a feature can based on the type of data, thetype of physiological data, the type of feature, the type of analysisbeing performed, or the like.

At block 610, the patient monitor encodes the selected category to beused when looking up the appropriate action in a lookup table. Thepatient monitor can encode the category in any number of different waysand use any number of different bits and/or bytes to do so. In anembodiment, the categories are represented as different sequences ofbits and/or bytes, or bit codes. The patient monitor uses the bit codesto look up the appropriate output in the lookup table. Other methods ofencoding the data are envisioned without departing from the spirit andscope of the description. For example, the different categories may berepresented using some alphanumeric code or word, or the like.

At determination block 612, the patient monitor determines if there areadditional features of the set of data to be analyzed. As describedabove in greater detail with reference to FIG. 4, more than one featurecan be used in the lookup table to select the appropriate action. Thus,at block 612, the patient monitor determines if an additional feature ofthe set of data is to be analyzed. If there is an additional feature ofthe set of data to be analyzed, the patient monitor determines theadditional feature of the set of data, as illustrated at block 614.Block 614 is similar to block 606, described above. At block 616, thepatient monitor determines a category within the additional feature ofthe set of data, similar to block 608, described above. At block 618,the patient monitor encodes the category to use with the lookup table,as described above with reference to block 610.

After encoding the category, as illustrated at block 618, the patientmonitor again determines if there is an additional feature to beanalyzed, as illustrated at block 612. If there are additional features,the patient monitor continues to analyze the additional feature(s),determine the category within the additional feature(s) and encode thecategory, as illustrated in blocks 614, 616, and 618, respectively. Oncethere are no additional features, the patient monitor looks up theaction corresponding to the one or more encoded categories, asillustrated at block 620.

To determine the appropriate action based on the encoded categories, thepatient monitor can use a lookup table, similar to the LUT 400 describedabove, with reference to FIG. 4. Using the lookup table, the patientmonitor can account for all of the different categories of the differentfeatures that were previously analyzed and determine the appropriateaction. In an embodiment, abstracting features and using the lookuptable reduces the number of computations processed by the patientmonitor.

At block 622, the patient monitor performs the appropriate action basedon the output of the lookup table. In an embodiment, the patient monitorrepeats process 600 as desired.

It is to be understood that the different actions that can be performedby the patient monitor are many. For example, the patient monitor maydetermine that the appropriate action includes changing a display oroutput, activating an alarm, gathering additional data via the sensor,the internet or some other means, notifying a healthcare provider, thepatient or another person, powering off, requesting additionalinformation from a user, etc. Thus, the various actions that may beperformed should be construed broadly.

Although described in terms of a patient monitor and physiological data,the processes described above, may be carried out using any number ofgeneral computing devices such as a personal computer, tablet, smartphone, and the like. As described above, abstracting features, orgeneral characteristics, of data and then performing some type of actionbased on the general characteristics, or features, of the data ratherthan the data itself can significantly decrease processing resources.The process can be useful whenever abstracting and processing featureswould use fewer processing resources than processing the data itself orwhere a number of different potentials options are available andundefined states would be harmful.

For example, in an embodiment, the table of FIG. 4 can be used inconjunction with a system that automatically changes displaycharacteristics based user proximity. In an embodiment in a system thattracks proximate users, the abstraction system described and table ofFIG. 4 can be used to determine screen changes based on proximate userpreferences. There can be a number of potential inputs used indetermining screen orientation. These can include proximity to thescreen in distance, hierarchy of proximate users, alarms relevant toproximate users, etc. Each of these inputs act as the signalabstractions and each will receive an appropriate bit code that can beused with the table of FIG. 4 to determine a solution. In an embodiment,use of the signal abstractions reduces the number nested if/thenstatements in software and simplifies the ability to encode for everypossible solution.

FIG. 7 illustrates an embodiment of a patient monitoring system 700capable of abstracting features of physiological data, as describedabove with reference to FIGS. 1-6. The patient monitoring system 700includes a patient monitor 702 attached to a sensor 706 by a cable 704.The sensor monitors various physiological data of a patient and sendssignals indicative of the one or more parameters to the patient monitor702 for processing.

The patient monitor 702 generally includes a display 708, controlbuttons 710, and a speaker 712 for audible alerts. The display 708 iscapable of displaying readings of various monitored patient parameters,which may include numerical readouts, graphical readouts, and the like.Display 708 may be a liquid crystal display (LCD), a cathode ray tube(CRT), a plasma screen, a Light Emitting Diode (LED) screen, OrganicLight Emitting Diode (OLED) screen, or any other suitable display. Thepatient monitor 702 may monitor SpO₂, Hb, HbO₂, SpHb™, SpCO®, SpOC™,SpMet®, PI, PVI®, PR, temperature, and/or other parameters.

An embodiment of a patient monitoring system 700 according to thepresent disclosure is capable of measuring and displaying trending dataof the various parameters and preferably is capable of conducting dataanalysis as to the trending. Furthermore, the patient monitoring systemis capable of abstracting features of the physiological data beingmonitored. In an embodiment, the patient monitor 702 includes anabstraction module for carrying out the processes described above. It isto be understood by one skilled in the art that the patient monitor 702may come in various, shapes, sizes and configurations without departingfrom the spirit and scope of the description. For example, the patientmonitor 702 may be larger, smaller, portable, comprise varying sizedisplays 708, and the like.

The sensor 706 may be one of many different types. For example, thesensor 706 may be disposable, reusable, multi-site, partially reusable,partially disposable, be adhesive or non-adhesive, monitor thephysiological parameters using reflectance, transmittance, ortransreflectance, and may be placed on a finger, hand, foot, forehead,neck, or ear, and may be a stereo sensor or a two-headed sensor. Thus,one of skill in the art will appreciate that sensor 706 may be anynumber of different types of sensors without departing from the spiritand scope of the disclosure.

FIG. 8 illustrates an example noninvasive multiparameter physiologicalmonitor 800 that can implement any of the features, processes, steps,etc., described herein. An embodiment of the monitor 800 includes adisplay 801 showing data for multiple physiological parameters. Forexample, the display 801 can include a CRT or an LCD display includingcircuitry similar to that available on physiological monitorscommercially available from Masimo Corporation of Irvine, Calif. soldunder the name Radical™, and disclosed in U.S. Pat. Nos. 7,221,971;7,215,986; 7,215,984 and 6,850,787, for example, the disclosures ofwhich are hereby incorporated by reference in their entirety. In anembodiment, the multiparameter patient monitor includes an abstractionmodule for performing the processes described above. Many other displaycomponents can be used that are capable of displaying respiratory rateand other physiological parameter data along with the ability to displaygraphical data such as plethysmographs, respiratory waveforms, trendgraphs or traces, and the like.

The depicted embodiment of the display 801 includes a measured value ofrespiratory rate 812 (in breaths per minute (bpm)) and a respiratoryrate waveform graph 806. In addition, other measured blood constituentsshown include SpO₂ 802, a pulse rate 804 in beats per minute (BPM), anda perfusion index 808. Many other blood constituents or otherphysiological parameters can be measured and displayed by themultiparameter physiological monitor 800, such as blood pressure, ECGreadings, EtCO₂ values, bioimpedance values, and the like. In someembodiments, multiple respiratory rates, corresponding to the multipleinput sensors and/or monitors, can be displayed.

FIG. 9 illustrates an embodiment of a sensor system 900 including asensor assembly 901 and a monitor cable 911 suitable for use with any ofthe physiological monitors shown in FIGS. 7 and 8. The sensor assembly901 includes a sensor 915, a cable assembly 917, and a connector 905.The sensor 915, in one embodiment, includes a sensor subassembly 902 andan attachment subassembly 904. The cable assembly 917 of one embodimentincludes a sensor 907 and a patient anchor 903. A sensor connectorsubassembly 905 is connected to the sensor cable 907.

The sensor connector subassembly 905 can be removably attached to aninstrument cable 911 via an instrument cable connector 909. Theinstrument cable 911 can be attached to a cable hub 920, which includesa port 921 for receiving a connector 912 of the instrument cable 911 anda second port 923 for receiving another cable. In certain embodiments,the second port 923 can receive a cable connected to a pulse oximetry orother sensor. In addition, the cable hub 920 could include additionalports in other embodiments for receiving additional cables. The hubincludes a cable 922 which terminates in a connector 924 adapted toconnect to a physiological monitor (not shown).

In an embodiment, the acoustic sensor assembly 901 includes a sensingelement, such as, for example, a piezoelectric device or other acousticsensing device. The sensing element can generate a voltage that isresponsive to vibrations generated by the patient, and the sensor caninclude circuitry to transmit the voltage generated by the sensingelement to a processor for processing. In an embodiment, the acousticsensor assembly 901 includes circuitry for detecting and transmittinginformation related to biological sounds to a physiological monitor.These biological sounds can include heart, breathing, and/or digestivesystem sounds, in addition to many other physiological phenomena. Theacoustic sensor 915 in certain embodiments is a biological sound sensor,such as the sensors described herein. In some embodiments, thebiological sound sensor is one of the sensors such as those described inthe '883 Application. In other embodiments, the acoustic sensor 915 is abiological sound sensor such as those described in U.S. Pat. No.6,661,161, which is incorporated by reference herein in its entirety.Other embodiments include other suitable acoustic sensors.

The attachment sub-assembly 904 includes first and second elongateportions 906, 908. The first and second elongate portions 906, 908 caninclude patient adhesive (e.g., in some embodiments, tape, glue, asuction device, etc.). The adhesive on the elongate portions 906, 908can be used to secure the sensor subassembly 902 to a patient's skin.One or more elongate members 910 included in the first and/or secondelongate portions 906, 908 can beneficially bias the sensor subassembly902 in tension against the patient's skin and reduce stress on theconnection between the patient adhesive and the skin. A removablebacking can be provided with the patient adhesive to protect theadhesive surface prior to affixing to a patient's skin.

The sensor cable 907 can be electrically coupled to the sensorsubassembly 902 via a printed circuit board (“PCB”) (not shown) in thesensor subassembly 902. Through this contact, electrical signals arecommunicated from the multi-parameter sensor subassembly to thephysiological monitor through the sensor cable 907 and the cable 911.

In various embodiments, not all of the components illustrated in FIG. 9are included in the sensor system 900. For example, in variousembodiments, one or more of the patient anchor 903 and the attachmentsubassembly 904 are not included. In one embodiment, for example, abandage or tape is used instead of the attachment subassembly 904 toattach the sensor subassembly 902 to the measurement site. Moreover,such bandages or tapes can be a variety of different shapes includinggenerally elongate, circular and oval, for example. In addition, thecable hub 920 need not be included in certain embodiments. For example,multiple cables from different sensors could connect to a monitordirectly without using the cable hub 920.

Additional information relating to acoustic sensors compatible withembodiments described herein, including other embodiments of interfaceswith the physiological monitor, are included in U.S. patent applicationSer. No. 12/044,883, filed Mar. 7, 2008, entitled “Systems and Methodsfor Determining a Physiological Condition Using an Acoustic Monitor,”and U.S. Pat. Application No. 61/366,866, filed Jul. 22, 2010, entitled“Pulse Oximetry System for Determining Confidence in Respiratory RateMeasurements,” the disclosures of which are hereby incorporated byreference in their entirety. An example of an acoustic sensor that canbe used with the embodiments described herein is disclosed in U.S. Pat.Application No. 61/252,076, filed Oct. 15, 2009, titled “Acoustic SensorAssembly,” the disclosure of which is hereby incorporated by referencein its entirety.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orstates. Thus, such conditional language is not generally intended toimply that features, elements and/or states are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or states are included or are to beperformed in any particular embodiment.

Depending on the embodiment, certain acts, events, or functions of anyof the methods described herein can be performed in a differentsequence, can be added, merged, or left out all together (e.g., not alldescribed acts or events are necessary for the practice of the method).Moreover, in certain embodiments, acts or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores, rather thansequentially.

The methods, steps, processes, calculations, computations or the like(“methods”) provided herein are simplified examples that are generallyperformed by advanced processing devices, including complex signalprocessors, sensitive analog and digital signal preprocessing boards,optical/optoelectronic componentry, display drivers and devices, orsimilar electronic devices. An artisan will recognize from thedisclosure herein that the various methods often must be performed atspeeds that, as a practical matter, could never be performed entirely ina human mind. Rather, for many calculations providing real time or nearreal time solutions, outputs, measurements, criteria, estimates, displayindicia, or the like, many of the foregoing processing devices performtens to billions or more calculations per second. In addition, suchprocessing devices may process electrical signals, infrared signals,wireless signals, or other electro-magnetic wave signals that areincomprehensible to a human mind in their raw form and at the speedscommunicated.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedherein can be implemented as electronic hardware, computer software, orcombinations of both operating in real-time or near real-time and atspeeds unattainable by a human. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. The described functionality can be implemented in varying waysfor each particular application, but such implementation decisionsshould not be interpreted as causing a departure from the scope of thedisclosure.

The various illustrative logical blocks, modules, and circuits describedin connection with the embodiments disclosed herein can be implementedor performed with a general purpose processor, a digital signalprocessor (DSP), an application specific integrated circuit (ASIC), afield programmable gate array (FPGA) or other programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.A general purpose processor can be a microprocessor, but in thealternative, the processor can be any conventional processor,controller, microcontroller, or state machine. A processor can also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The blocks of the methods and algorithms described in connection withthe embodiments disclosed herein can be embodied directly in hardware,in a software module executed by a processor, or in a combination of thetwo. A software module can reside in RAM memory, flash memory, ROMmemory, EPROM memory, EEPROM memory, registers, a hard disk, a removabledisk, a CD-ROM, or any other form of computer-readable storage mediumknown in the art. An exemplary storage medium is coupled to a processorsuch that the processor can read information from, and write informationto, the storage medium. In the alternative, the storage medium can beintegral to the processor. The processor and the storage medium canreside in an ASIC. The ASIC can reside in a user terminal. In thealternative, the processor and the storage medium can reside as discretecomponents in a user terminal.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it will beunderstood that various omissions, substitutions, and changes in theform and details of the devices or algorithms illustrated can be madewithout departing from the spirit of the disclosure. As will berecognized, certain embodiments of the inventions described herein canbe embodied within a form that does not provide all of the features andbenefits set forth herein, as some features can be used or practicedseparately from others. The scope of certain inventions disclosed hereinis indicated by the appended claims rather than by the foregoingdescription. All changes which come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

What is claimed is:
 1. A method for reducing processing load of apatient monitor, the method comprising: emitting light from a lightemitting element of a physiological sensor; detecting at thephysiological sensor light from the light emitting element after it hasbeen attenuated by tissue; receiving at a patient monitor physiologicaldata from the physiological sensor based at least in part on thedetected light; calculating at the patient monitor in real-time a firstvalue for each feature of one or more features of the physiologicaldata, wherein each feature comprises a plurality of categories;assigning the physiological data to a category of the plurality ofcategories for each feature based on a respective first value of theeach feature; assigning a second value to the physiological data foreach feature based on the assigned category from the plurality ofcategories; determining, using an electronic lookup table, an actionfrom a set of actions to perform based on an aggregation of each secondvalue from each of the one or more features; and activating an alarmbased at least in part on the determined action.
 2. The method of claim1, wherein the one or more features comprise at least one of a standarddeviation of the physiological data, a slope of the physiological data,a change in the slope of the physiological data, a correlationcoefficient of the physiological data, a linear regression of thephysiological data, and a comparison of outputs between differentprocessing engines.
 3. The method of claim 1, wherein the set of actionsincludes at least one of changing a display of a patient monitor,changing an output of the patient monitor, receiving additional datafrom the physiological sensor, receiving additional data from theinternet, notifying a healthcare provider, notifying the patient,powering off, and requesting additional information from a user.
 4. Themethod of claim 1, wherein the one or more features are predetermined.5. The method of claim 1, further comprising selecting the one or morefeatures from a predetermined set of features.
 6. The method of claim 5,wherein selecting the one or more features comprises selecting all thefeatures on the list.
 7. The method of claim 5, wherein selecting theone or more features comprises selecting a subset of the features on thelist.
 8. The method of claim 5, wherein selecting the one or morefeatures comprises selecting the one or more features that use fewerprocessing resources.
 9. The method of claim 5, wherein selecting theone or more features comprises selecting the one or more features basedon a type of physiological data obtained.
 10. The method of claim 1,wherein the physiological sensor is one of a pulse oximeter sensor andan acoustic sensor.
 11. A patient monitor system comprising: aphysiological sensor configured to emit light towards a tissue site,detect the light after it has been attenuated by tissue, and obtainphysiological data based at least in part on the detected light; and apatient monitor in communication with the physiological sensor andconfigured to: receive the physiological data from the physiologicalsensor; determine one or more features of the physiological data;determine a category for the physiological data within each of the oneor more features of the physiological data; associate an action with thephysiological data based on the one or more determined categories usingan electronic lookup table, wherein associating the action with thephysiological data is based on an aggregation of the determinedcategories when there are multiple determined categories; and activatean alarm based at least in part on the determined action.
 12. Thepatient monitor system of claim 11, wherein the patient monitordetermines in real-time the one or more features of the physiologicaldata.
 13. The patient monitor system of claim 11, wherein the one ormore features comprise at least one of a standard deviation of thephysiological data, a slope of the physiological data, a change in theslope of the physiological data, and a comparison of outputs betweendifferent processing engines.
 14. The patient monitor system of claim11, wherein the one or more features are predetermined.
 15. The patientmonitor system of claim 11, wherein the patient monitor is furtherconfigured to select the one or more features from a predetermined listof features, wherein the patient monitor determines the one or morefeatures selected from the predetermined list of features.
 16. Thepatient monitor system of claim 15, wherein the patient monitor selectsthe one or more features by selecting all the features on the list. 17.The patient monitor system of claim 15, wherein patient monitor selectsthe one or more features by selecting a subset of the features on thelist.
 18. The patient monitor system of claim 15, wherein patientmonitor selects the one or more features by selecting the one or morefeatures that use fewer processing resources.
 19. The patient monitorsystem of claim 15, wherein patient monitor selects the one or morefeatures by selecting the one or more features based on a type ofphysiological data obtained.
 20. The patient monitor system of claim 11,wherein the physiological sensor is one of a pulse oximeter sensor andan acoustic sensor.
 21. A computer-implemented method for reducingprocessing load of a patient monitor, the method comprising: under thecontrol of one or more computing devices of a patient monitor: receivingphysiological data from a physiological sensor configured to emit lighttowards a tissue site, detect the light after it has been attenuated bytissue, and obtain the physiological data based at least in part on thedetected light; determining one or more features of the physiologicaldata; determining a category for the physiological data within each ofthe one or more features of the physiological data; associating anaction with the physiological data based on the one or more determinedcategories, wherein associating the action with the physiological datais based on an aggregation of the determined categories when there aremultiple determined categories; and activating an alarm based at leastin part on the determined action.
 22. The method of claim 21, whereinthe action associated with the physiological data is based on the outputof an electronic lookup table stored in a computer-readable medium. 23.The method of claim 21, wherein the determining one or more features ofthe physiological data occurs in real-time.